Generating personalized banner images using machine learning

ABSTRACT

A machine is configured to generate in real time personalized online banner images for users based on data pertaining to user behavior in relation to an image of a product. For example, the machine receives a user selection indicating one or more data features associated with the user. The one or more data features include a data feature pertaining to user behavior in relation to an image of a product. The machine generates, using a machine learning algorithm, a data representation of the machine learning algorithm based on the one or more data features including the data feature pertaining to user behavior in relation to the image of the product. The data representation includes one or more data features pertaining to one or more characteristics of online banner images. The machine generates an online banner image for the user based on the data representation.

This application is a continuation of U.S. patent application Ser. No.16/155,255, filed Oct. 9, 2018, the contents of which is hereinincorporated by reference in its entirety.

TECHNICAL FIELD

Embodiments of the present disclosure relate generally to generatingpersonalized banner images using machine learning, and, moreparticularly, but not by way of limitation, to generating an enhanceduser interface for displaying an online banner image that ispersonalized to appeal to a particular user.

BACKGROUND

Currently, online advertising (e.g., an online banner image, or simply“online banner”) is generated for display to a user based on a priorsearch by the user using some search words. Such online advertising mayinclude a stock image of a product that represents results of the searchperformed using the search words. A stock image of a product may notaccurately represent the product in which the user is interested, andtherefore may not capture the user's attention, and may not result in anintended result (e.g., a click on the online advertising, or a purchaseof the displayed product). In addition, the banner images are oftenpre-defined and generated in advance, and by the time the online bannersare shown to users they are not relevant to the users anymore.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present disclosure and cannot be considered aslimiting its scope.

FIG. 1 is a block diagram illustrating a networked system, according tosome example embodiments.

FIG. 2 illustrates the training and use of a machine-learning program,according to some example embodiments.

FIG. 3 illustrates a user interface displaying an online personalizedbanner image, according to some example embodiments.

FIG. 4 is a block diagram illustrating components of a machine learningsystem, according to some example embodiments.

FIG. 5 is a flow diagram illustrating a method for generatingpersonalized banner images using machine learning, according to someexample embodiments.

FIG. 6 is a flow diagram illustrating a method for generatingpersonalized banner images using machine learning, and representing anadditional step of the method illustrated in FIG. 5, according to someexample embodiments.

FIG. 7 is a flow diagram illustrating a method for generatingpersonalized banner images using machine learning, and representingadditional steps of the method illustrated in FIG. 6, according to someexample embodiments.

FIG. 8 is a flow diagram illustrating a method for generatingpersonalized banner images using machine learning, and representing step504 of the method illustrated in FIG. 5 in more detail, according tosome example embodiments.

FIG. 9 is a block diagram illustrating an example of a softwarearchitecture that may be installed on a machine, according to someexample embodiments.

FIG. 10 illustrates a diagrammatic representation of a machine in theform of a computer system within which a set of instructions may beexecuted for causing the machine to perform any one or more of themethodologies discussed herein, according to an example embodiment.

The headings provided herein are merely for convenience and do notnecessarily affect the scope or meaning of the terms used.

DETAILED DESCRIPTION

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

A machine learning system may facilitate the generation, usingmachine-learning algorithms, of online banners based on actual images(e.g., photographs) extracted from product listings available on awebsite (e.g., the website of an online store or online marketplace).The images correspond to products at which the user looked previously,for instance, in a prior visit to the website. The user may havesearched for these products or may have clicked on informationassociated with these products displayed to the user while navigatingthe website.

In addition to the online banners including images from listings ofactual products available for purchase, the online banners are generatedto aesthetically appeal to particular users or to groups of users basedon continuous machine learning of what types of online banners areselected by various users. For example, an online banner that includesparticular structural elements (e.g., a green rectangle online bannerdivided vertically into three equal portions where the left portionincludes text, and the middle and tight portions each includes an imageof a particular product) may be displayed to a number of users and maybe determined to be more appealing to a certain demographic. Certainfeatures of the online banner may be iteratively modified in nextversions of the online banner displayed to the users in order tocontinue to learn about the user response to various featurecombinations associated with the online banners.

The continuous learning allows the machine-learning system toiteratively update data representations (e.g., embeddings) of themachine-learning system associated with the users in order to achieve abetter understanding of the users' likes and dislikes with respect tothe various aspects of the online banners. As a result, themachine-learning system generates highly-customized online banners thatappeal to (e.g., are highly selectable by) groups of users or particularusers.

For example, A/B tests are utilized to collect user data pertaining tothe online banners (e.g., click or lack of a click on a particularonline banner, or conversion rate for the products displayed in productimages included in the online banners). Based on the user data (e.g.,user feedback on online banner design choices), the machine learningsystem, using one or more reinforcement learning algorithms, iterativelymodifies various aspects of the online banner images in order tooptimize the banner advertisement click rate and conversion rate.

In some example embodiments, based on determining that some onlinebanners are being selected (e.g., clicked on), the machine learningsystem learns not only about users in general but also about particularusers. In the next iteration (e.g., next online banner shown to a user),the machine learning system modifies one feature of the online bannerpreviously shown to the user to determine whether the user clicks on themodified online banner or not. That is helpful in understanding wherethe user's preference is with respect to the modified feature of thebanner. This learning facilitates increased level of customization ofonline banners to users' specific preferences regarding product imagesand esthetic qualities of online banners.

In some example embodiments, the machine learning system identifies theonline banners that are more successful by ranking the online bannersbased on the number of times the respective online banners were selectedby users. The combinations of features associated with the moresuccessful online banners are associated with certain weight valuesbased on the ranking of the online banners. The machine learning systemalso analyzes the combinations of features of the more successful onlinebanners to determine whether some online banners appeal to certaingroups of people grouped demographically, geographically, byprofessions, by neighborhood culture, etc. The machine learning systemmay segment a user population in a variety of ways to learn what featurecombinations appeal to various segments of the user population.

Generally, web sites that publish digital content pertaining to items ofinterest to the public present such digital content only as listingsthat include various information about the items. An example of suchdigital content is a listing published on behalf of a seller of aproduct. A user interface of a client device presents listings ofproducts that may include one or more photographs of the product and adescription of one or more attributes of the product.

Conventional user interfaces have many deficits relating to theefficient functioning of the computer, requiring a user of aconventional user interface to scroll around and switch views many timesto find the right data associated with an item, especially when theconventional user interface is displayed on a small screen. Becausesmall screens tend to need data and functionality divided into manylayers or views, conventional user interfaces require users to drilldown through many layers to get to desired data or functionality. Thatprocess could seem slow, complex, and difficult to learn, particularlyto novice users. Further, that process decreases data processing speeds,and is often associated with higher data storage requirements.

In some example embodiments, a machine learning system that providesiteratively updated online banners displayed via a user interfaceimproves conventional user interfaces by presenting the informationpertaining to the items illustrated in the item images included in theonline banners in a particular way in electronic devices that results inthe delivery of exact information pertaining to a particular item ofactual interest to the user at the time a visualization of the onlinebanner is presented to the user. The improved functionality of the userinterface of the electronic device also enhances the efficiency of theelectronic devices by improving data processing speeds and data storageefficiency.

With reference to FIG. 1, an example embodiment of a high-levelclient-server-based network architecture 100 is shown. A networkedsystem 102 provides server-side functionality via a network 104 (e.g.,the Internet or wide area network (WAN)) to one or more client devices110. FIG. 1 illustrates, for example, a web client 112 (e.g., a browser,such as the Internet Explorer® browser developed by Microsoft®Corporation of Redmond, Wash. State), a client application 114, and aprogrammatic client 116 executing on client device 110.

The client device 110 may comprise, but is not limited to, mobilephones, desktop computers, laptops, portable digital assistants (PDAs),smart phones, tablets, ultra books, netbooks, multi-processor systems,microprocessor-based or programmable consumer electronics, gameconsoles, set-top boxes, wearable devices, smart watches, or any othercommunication devices that a user may utilize to access the networkedsystem 102. In some embodiments, the client device 110 may comprise adisplay module to display information (e.g., in the form of userinterfaces). In further embodiments, the client device 110 comprises oneor more of a touch screens, accelerometers, gyroscopes, cameras,microphones, global positioning system (GPS) devices, and so forth. Theclient device 110 is a device of a user that can be used to perform atransaction involving digital items within the networked system 102. Insome example embodiments, the networked system 102 comprises anetwork-based marketplace (also referred to as “online marketplace”)that responds to requests for product listings, publishes publicationscomprising item listings of products or services available on thenetwork-based marketplace, and manages payments for these marketplacetransactions. One or more portions of network 104 may be an ad hocnetwork, an intranet, an extranet, a virtual private network (VPN), alocal area network (LAN), a wireless LAN (WLAN), a wide area network(WAN), a wireless WAN (WWAN), a metropolitan area network (MAN), aportion of the Internet, a portion of the Public Switched TelephoneNetwork (PSTN), a cellular telephone network, a wireless network, a WiFinetwork, a WiMax network, another type of network, or a combination oftwo or more such networks.

The client device 110 includes one or more applications (also referredto as “apps”) such as, but not limited to, a web browser, messagingapplication, electronic mail (email) application, an e-commerce siteapplication (also referred to as a marketplace application), and thelike. In some embodiments, if the e-commerce site application isincluded in the client device 110, then this application is configuredto locally provide the user interface and at least some of thefunctionalities with the application configured to communicate with thenetworked system 102, on an as needed basis, for data or processingcapabilities not locally available (e.g., to access to a database ofitems available for sale, to authenticate a user, to verify a method ofpayment, etc.). Conversely, if the e-commerce site application is notincluded in the client device 110, the client device 110 uses its webbrowser to access the e-commerce site (or a variant thereof) hosted onthe networked system 102.

One or more users 106 may be a person, a machine, or other means ofinteracting with the client device 110. In example embodiments, the user106 is not part of the network architecture 100, but may interact withthe network architecture 100 via the client device 110 or other means.For instance, the user 106 provides input (e.g., touch screen input oralphanumeric input) to the client device 110 and the input iscommunicated to the networked system 102 via the network 104. In thisinstance, the networked system 102, in response to receiving the inputfrom the user 106, communicates information to the client device 110 viathe network 104 to be presented to the user 106. In this way, the user106 can interact with the networked system 102 using the client device110.

An application program interface (API) server 120 and a web server 122are coupled to, and provide programmatic and web interfaces respectivelyto, one or more application servers 140. The application servers 140 mayhost a machine learning system 400 and a payment system 144, each ofwhich may comprise one or more modules or applications and each of whichmay be embodied as hardware, software, firmware, or any combinationthereof. The application servers 140 are, in turn, shown to be coupledto one or more database servers 124 that facilitate access to one ormore information storage repositories or databases 126. In an exampleembodiment, the databases 126 are storage devices that store information(e.g., publications, listings, digital content items, productdescriptions, images of products, etc.) to be utilized by the machinelearning system 400. The databases 126 may also store digital iteminformation, in accordance with example embodiments.

Additionally, a third party application 132, executing on one or morethird party servers 130, is shown as having programmatic access to thenetworked system 102 via the programmatic interface provided by the APIserver 120. For example, the third party application 132, utilizinginformation retrieved from the networked system 102, supports one ormore features or functions on a website hosted by the third party. Thethird party website, for example, provides one or more promotional,marketplace, or payment functions that are supported by the relevantapplications of the networked system 102.

The machine learning system 400 provides a number of publicationfunctions and services to users 106 that access the networked system102. For example, the machine learning system 400 facilitates thegeneration and online publishing of a customized banner image for aparticular user based on one or more data features associated with theparticular user. The one or more data features include a data featurepertaining to user behavior in relation to an image of a product. Thepayment system 144 provides a number of functions to perform orfacilitate payments and transactions. While the machine learning system400 and payment system 144 are shown in FIG. 1 to both form part of thenetworked system 102, it will be appreciated that, in alternativeembodiments, each of the machine learning system 400 and payment system144 may form part of a service that is separate and distinct from thenetworked system 102. In some embodiments, the payment system 144 mayform part of the machine learning system 400.

Further, while the client-server-based network architecture 100 shown inFIG. 1 employs a client-server architecture, the present inventivesubject matter is of course not limited to such an architecture, andcould equally well find application in a distributed, or peer-to-peer,architecture system, for example. The machine learning system 400 andpayment system 144 could also be implemented as standalone softwareprograms, which do not necessarily have networking capabilities.

The web client 112 accesses the machine learning system 400 or thepayment system 144 via the web interface supported by the web server122. Similarly, the programmatic client 116 accesses the variousservices and functions provided by the machine learning system 400 orthe payment system 144 via the programmatic interface provided by theAPI server 120. The programmatic client 116 may, for example, be aseller application (e.g., the Turbo Lister application developed by eBayInc., of San Jose, Calif.) to enable sellers to author and managelistings on the networked system 102 in an off-line manner, and toperform batch-mode communications between the programmatic client 116and the networked system 102.

FIG. 2 illustrates the training and use of a machine-learning program,according to some example embodiments. In some example embodiments,machine-learning programs (MLP), also referred to as machine-learningalgorithms or tools, are utilized to perform operations associated withsearches, such as digital content (e.g., an image, a productdescription, or a listing) searches, and optimization operations.

Machine learning is a field of study that gives computers the ability tolearn without being explicitly programmed. Machine learning explores thestudy and construction of algorithms (also referred to herein as“tools”) that learns from existing data and makes predictions about newdata. Such machine-learning tools operate by building a model fromexample training data 212 in order to make data-driven predictions ordecisions expressed as outputs or assessments 220. Although exampleembodiments are presented with respect to a few machine-learning tools,the principles presented herein may be applied to other machine-learningtools.

In some example embodiments, different machine-learning tools are used.For example, Logistic Regression (LR), Naive-Bayes, Random Forest (RF),neural networks (NN), matrix factorization, and Support Vector Machines(SVM) tools may be used for generating online banner images that areoptimized to appeal to particular users.

In general, there are two types of problems in machine learning:classification problems and regression problems. Classificationproblems, also referred to as categorization problems, aim atclassifying items into one of several category values (e.g., is thisobject an apple or an orange?). Regression algorithms aim at quantifyingsome items (e.g., by providing a value that is a real number). In someembodiments, example machine-learning algorithms provide a score (e.g.,a number from 1 to 100) to qualify one or more products as a match for auser of the online marketplace.

In certain example embodiments, the machine-learning algorithms utilizethe training data 212 to find correlations among identified features 202(or combinations of features 202) that affect the outcome. In someexample embodiments, example machine-learning algorithms are used todetermine what colors to use in an online banner image, what colorcombinations appeal to users, what proportions (e.g., measurements orratios) associated with the different sections included in the onlinebanner image appeal to users, what type, size, color, and font of textusers respond to, and what various features associated with productimages elicit desired responses from users (e.g., purchases of products,or selection of online banner images). Random combinations of features(e.g., one overall color of the online banner image or not, the numberof portions in the online banner image, color of each portion in theonline banner image, number of different products in the online bannerimage, if only one product is used—the number of times a product imageappears in the online banner image, where in the online banner image theproduct image appears, or various text-related features) are used astraining data 212. A machine-learning program executes to generate anumber of online banner images using the various combinations offeatures. In some example embodiments, an administrator of the machinelearning system 400 determines which of the various combinations offeatures are great (e.g., lead to desired results), and which ones arenot. The combinations of features determined to be (e.g., classified as)successful are input into a machine learning algorithm for the machinelearning algorithm to learn which combinations of features (alsoreferred to as “patterns”) are “good” (e.g., a user will select theonline banner image), and which patterns are “bad.”

The interactions by users with various online banners provide anotherfeature for the machine learning algorithm to add to the identifiedfeatures 202, and use in learning which banner patterns are preferred byusers—which color pattern/combination are preferred; whether usersprefer online banners that include more than one product imagespreferred to online banners that includes only one product image;whether users prefer to see a lot of one color (e.g., pink) in theonline banner; whether users prefer online banners without a productimage, etc. The machine learning system then starts to generatethousands and thousands of banners for the same combination of features,and based on the continuous learning picks the best banners to show tousers at particular times based on the learning performed up to thatpoint.

The machine-learning algorithms utilize features for analyzing the datato generate assessments 220. A feature 202 is an individual measurableproperty of a phenomenon being observed. The concept of feature isrelated to that of an explanatory variable used in statisticaltechniques such as linear regression. Choosing informative,discriminating, and independent features is important for effectiveoperation of the MIT in pattern recognition, classification, andregression. Features may be of different types, such as numeric,strings, and graphs.

In some example embodiments, the features 202 may be of different typesand include one or more of user features 204, item features 206,preference features 208, and online banner features 210. The userfeatures 204 includes one or more features, such as a user identifier(e.g., a name or a login), whether the user is a buyer or a seller, orboth, a gender of the user, a location of the user, a language of theuser, data pertaining to one or more purchases by the user, identifiersof items watched by the user on the online marketplace, identifiers ofliked items on the online marketplace, items placed into an online cartof the online marketplace, etc.

The item features 206 includes any data related to an item, such as aproduct available for sale on the online marketplace. Example of itemfeatures 206 are an item identifier (e.g., a product identifier, such asa stock keeping unit (SKU)), a type of item, a brand of the item, a sizeof the item, a color of the item, an image of the item, etc.

The preference features 208 include any data related to the preferencesof a user, such as express preferences provided by the user, or impliedor derived preferences of the user (e.g., preferences of the userextracted from recommendations provided by the user to other users).

The online banner features 210 include a template data feature thatidentifies a template for generating an online banner image, a frequencyfeature that identifies how frequently the online banner image should bepresented to the user, a result feature that indicates whether a certainonline banner image is associated with a desired result (e.g., aninteraction by the user with the online banner image).

The machine-learning algorithms utilize the training data 212 to findcorrelations among the identified features 202 that affect the outcomeor assessment 220. In some example embodiments, the training data 212includes known data for one or more identified features 202 and one ormore outcomes, such as combinations of features in an online bannerimage which lead to users selecting the online banner image,combinations of features in an online banner image which lead to userspurchasing the product displayed in a product image included in theonline banner image, etc.

With the training data 212 and the identified features 202, themachine-learning tool is trained at operation 214. The machine-learningtool appraises the value of the features 202 as they correlate to thetraining data 212. The result of the training is the trainedmachine-learning program 216.

When the machine-learning program 216 is used to perform an assessment,new data 218 is provided as an input to the trained machine-learningprogram 216, and the machine-learning program 216 generates theassessment 220 as output. For example, when a user selects a particularonline banner image displayed for the user in a user interface of aclient device of the user, a machine learning program, trained withvarious combinations of features used to generate various online bannerimages, updates the features 202 with one or more additional features(e.g., a feature that indicates that the user selected the particularonline banner image) which will be used in further training of themachine-learning program, and in the generating of future personalizedonline banner images for the particular user and possibly other users.

FIG. 3 illustrates a user interface 300 that is improved by displaying aselectable personalized online banner image 310 to a user of a clientdevice, according to various example embodiments. The online bannerimage 310 includes one or more sections (e.g., section 312, section 314,or section 316). The online banner image 310 may be divided intohorizontal sections, vertical sections, or a combination thereof. Insome example embodiments, each section of the online banner image 310has the same background color. In some example embodiments, thebackground colors of the various sections are different. In variousexample embodiments, the background colors of the various sections areshades of the same color.

One or more sections of the online banner image 310 include text. Asshown in FIG. 3, section 312 of the online banner image 310 includestext 318. The placement of the text, the type and size of the fontassociated with the text may be different, according to various exampleembodiments.

One or more sections of the online banner image 310 include one or moreimages of one or more items. As shown in FIG. 3, section 314 includesimage 320, and section 312 includes image 322. The one or more items, insome instances, are products available for sale on an onlinemarketplace. In some example embodiments, the one or more images areproduct images previously viewed by the user. A product image previouslyviewed by the user may be included in the online banner image topersonalize the online banner image to an interest recently manifested(e.g., expressed or displayed) by the user while browsing a website ofthe online marketplace.

In some example embodiments, image 320 and image 322 are the same exactimage of a particular product (e.g., the images are repeated in theonline banner image 310). In certain example embodiments, image 320 andimage 322 are two different images of a particular product (e.g., theimages display different perspectives of the particular product in theonline banner image 310). In various example embodiments, image 320 andimage 322 are images of two different products of a particular type(e.g., image 320 illustrates a first type of shoe, and image 322illustrates a second type of shoe). In some example embodiments, image320 and image 322 are images of two different products of two differenttypes, where the types of product belong to the same category, such as aparticular sport (e.g., image 320 illustrates a hockey stick, and image322 illustrates a hockey puck). In various example embodiments, image320 and image 322 are images of two different products of two differenttypes, where the types of product belong to different categories ofproducts (e.g., image 320 illustrates a hockey stick, and image 322illustrates a sculpture).

The user interface 300 may display additional information, such ascontent item 318 or content item 320. In some example embodiments, theadditional information includes information pertaining to one or moreproducts available for sale on the online marketplace.

In various example embodiments, the user selects the online banner image310 (e.g., one or more portions of the online banner image 310). Aselection of the online banner image 310 results in the causing ofdisplay of additional information pertaining to the product illustratedin one of the images 320 or 322. For example, the machine learningsystem generates a personalized online banner 310 to include images 320or 322 that illustrate a particular antique chair that the userpreviously viewed on the online marketplace. The user selects one of theimages 320 or 322 included in the online banner image 310. Based on theuser selection of one of the images 320 or 322, the machine learningsystem accesses a product listing associated with the particular antiquechair, and causes display of the product listing as content item 318 inthe user interface 300. The product listing may include one or more ofthe images 320 and 322, and a description of one or more attributes ofthe product.

In some example embodiments, the online banner image 310 is caused todisplay in a physical billboard (e.g., a billboard that displaysinformation on the side of a road), an electronic billboard (e.g., abillboard that displays information via a user interface of a clientdevice), or an electronic physical billboard (e.g., an electronicbillboard located in a public place, that allows a user to interact withone or more parts of the electronic billboard to obtain additionalinformation pertaining to the data displayed on the electronicbillboard). In various example embodiments, the information displayedvia such billboards is customized to a market or population of aparticular geographic area (e.g., a city, a state, or a country).

FIG. 4 is a block diagram illustrating components of the machinelearning system 400, according to some example embodiments. As shown inFIG. 4, the machine learning system 400 includes an access module 402, adata representation generating module 404, a banner image generatingmodule 406, and a user interface module 408, all configured tocommunicate with each other (e.g., via a bus, shared memory, or aswitch).

According to some example embodiments, the access module 402 receives auser selection indicating one or more data features associated with theuser. The one or more data features include a data feature pertaining touser behavior in relation to an image of a product.

The data representation generating module 404 generates, using a machinelearning algorithm, a data representation of the machine learningalgorithm. The generating of the data representation is based on the oneor more data features including the data feature pertaining to userbehavior in relation to the image of the product. The datarepresentation includes one or more data features pertaining to one ormore characteristics of online banner images. The data representationmay be stored in a record of database.

The banner image generating module 406 generates an online banner imagefor the user based on the data representation. In some exampleembodiments, one or more of the features 202 of FIG. 2 are included inthe data representation, and provide (e.g., represent or indicate)information used by the banner image generating module 406 in generatingthe online banner image. In some example embodiments, the banner imagegenerating module 406 utilizes computer vision techniques. In variousexample embodiments, the generated online banner image includes one ormore sections, as shown in FIG. 3.

The user interface module 408 causes display of the online banner imagein a user interface of a client device associated with the user. In someexample embodiments, the user interface module 408 causes display of theonline banner image in an electronic physical billboard, such as abillboard displayed at a bus station, a conference, in the lobby of abuilding, in a store, or in other public places. The online banner imagecaused to be displayed in the electronic physical billboard may includeone or more sections, as described in FIG. 3. The electronic physicalbillboard may provide the online banner image in a user interface of theelectronic physical billboard to allow a user to select one or moreelements of the user interface, or the one or more sections of theonline banner image. The one or more items included in images displayedin the online banner image caused to be displayed in the electronicphysical billboard are, in some instances, items of interest to segmentof population associated with (e.g., living in, working in, or visiting)the neighborhood where the electronic physical billboard is located.

To perform one or more of its functionalities, the machine learningsystem 400 communicates with one or more other systems. For example, anintegration engine (not shown) may integrate the machine learning system400 with one or more email server(s), web server(s), one or moredatabases, or other servers, systems, or repositories.

Any one or more of the modules described herein may be implemented usinghardware (e.g., one or more processors of a machine) or a combination ofhardware and software. For example, any module described herein mayconfigure a processor (e.g., among one or more processors of a machine)to perform the operations described herein for that module. In someexample embodiments, any one or more of the modules described herein maycomprise one or more hardware processors and may be configured toperform the operations described herein. In certain example embodiments,one or more hardware processors are configured to include any one ormore of the modules described herein.

Moreover, any two or more of these modules may be combined into a singlemodule, and the functions described herein for a single module may besubdivided among multiple modules. Furthermore, according to variousexample embodiments, modules described herein as being implementedwithin a single machine, database, or device may be distributed acrossmultiple machines, databases, or devices. The multiple machines,databases, or devices are communicatively coupled to enablecommunications between the multiple machines, databases, or devices. Themodules themselves are communicatively coupled (e.g., via appropriateinterfaces) to each other and to various data sources to allowinformation to be passed between the applications and to allow theapplications to share and access common data. Furthermore, the modulesmay access one or more of the databases 226.

FIGS. 5-8 are flowcharts illustrating a method for generatingpersonalized banner images using machine learning, according to someexample embodiments. Operations in the method 500 illustrated in FIG. 5may be performed using modules described above with respect to FIG. 4.As shown in FIG. 5, method 500 may include one or more of methodoperations 502, 504, and 506, according to example embodiments.

At operation 502, the access module 402 receives a user selectionindicating one or more data features associated with the user. The oneor more data features include a data feature pertaining to user behaviorin relation to an image of a product. In some example embodiments, theuser selection includes a selection of an image of an item on a website(e.g., a product image of a product available for purchase on an onlinemarketplace).

At operation 504, the data representation generating module 404generates, using a machine learning algorithm, a data representation ofthe machine learning algorithm. The generating of the datarepresentation is based on the one or more data features including thedata feature pertaining to user behavior in relation to the image of theproduct. The data representation includes one or more data featurespertaining to one or more characteristics of online banner images.

In some example embodiments, the data representation of the machinelearning algorithm is a vector of data features. The vector includes theone or more data features associated with the user. The vector furtherincludes one or more data features associated with a look-and-feel ofthe online banner image. The look-and-feel of the online banner imageis, in some instances, specified by a template data feature thatrepresents one or more structural elements of the online banner image.Examples of structural elements of the online banner image are anidentifier of the number of sections of the online banner image, anindication of the location of the various sections within the onlinebanner image, identifiers of background colors of the various sectionsof the online banner image, an identifier of a particular product imageassociated with a product, such as a product available for purchase onthe online marketplace, text to be included in the online banner image,etc.

At operation 506, the banner image generating module 406 generates anonline banner image for the user based on the data representation. Theonline banner image may be presented to a user via a user interface of aclient device of the user. An example online banner image is shown inFIG. 3 above.

In some example embodiments, the machine learning system 400 leveragesnatural language processing and computer vision technologies to generatean online banner image for the user in real time based on a prior search(e.g., a search for data pertaining to a product) by the user at theonline marketplace website.

In various example embodiments, the online banner image, in some exampleembodiments, is caused to display in a particular (e.g., a first) areaof the user interface. Additional information may be presented to theuser in other (e.g., a second or a third) areas of the user interface.

In some example embodiments, the online banner image includes a productimage that is selectable via the user interface. In some instances, aselection, via the user interface, of the product image results incausing display, in the user interface, of a product listing associatedwith a product depicted in the product image. The product listing isselectable via the user interface. In response to the user selecting theproduct listing, the user interface module 408 accesses data associatedwith the product listing from a record of a database, and causes displayof the data associated with the product listing in the user interface(e.g., in a second area of the user interface) of the client device. Insome instances, the product information is caused to be displayed suchthat the product information replaces the online banner image in theuser interface. In some instances, the product information is caused tobe displayed such that the product information is displayed togetherwith the online banner image in the user interface. For example, theonline banner image is displayed in a first area of the user interface,while the product information is displayed in a second area of the userinterface.

In some example embodiments, the one or more data features include afeature indicating a product previously searched by the user. Forexample, during a first user session on an online marketplace, the userlogs in and browses a number of web pages associated with one or moreproducts available for purchase via the online marketplace. The user mayspend a time that exceeds a particular threshold value viewing aparticular product (e.g., a pair of shoes, a vase, or a piece of art).Next time the user logs into the website associated with the onlinemarketplace, the machine learning system 400 generates and causes to bedisplayed in the user interface an online banner image that includes theactual one or more images of the particular product that the user viewedin the prior user session at the online marketplace.

Further details with respect to the method operations of the method 500are described below with respect to FIGS. 6-8.

As shown in FIG. 6, method 500 includes operation 602, according to someembodiments. Operation 602 may be performed after operation 506, inwhich the user interface module 330 causes display of the online bannerimage in a user interface of a client device associated with the user.

In some example embodiments, the online banner image is caused to bedisplayed in the user interface of the client device based on receivingone or more login credentials from the client device.

The causing display of the online banner image enhances the userinterface on the client device by presenting a selectable online bannerimage in the user interface. The user may select (e.g., click on) anyportion of the online banner image, and may be shown informationpertaining to a product illustrated in the product image included in theselected online banner image. In some example embodiments, the machinelearning system 400 causes display of a listing of the productillustrated in the product image included in the selected online bannerimage.

As shown in FIG. 7, method 500 includes operations 702, 704, and 706,according to some embodiments. Operation 702 is performed afteroperation 602 of FIG. 6, in which the user interface module 330 causesdisplay of the online banner image in a user interface of a clientdevice associated with the user.

At operation 702, the access module 402 receives, from the clientdevice, an indication of an input. In some example embodiments, theinput is a selection, by the user, of the online banner image displayedin the user interface of the client device associated with the user.

At operation 704, the data representation generating module 404 updatesthe data representation of the machine learning algorithm. In someexample embodiments, the updating of the data representation is based onadding an additional feature that indicates the input. In variousexample embodiments, the updating of the data representation is based onchanging an existing feature included in the data representation inorder to indicate the input received from the client device.

At operation 706, the banner image generating module 406 updates theonline banner image for the user based on the updated datarepresentation. In some example embodiments, the updating of the onlinebanner image includes generating a further online banner image for theuser to reflect additional information pertaining to the user (e.g., anadditional action by the user, or an additional preference expressed bythe user or derived based on data pertaining to the user).

In some example embodiments, the indication of an input includes anindication of a lack of selection by a user of the online banner. Forexample, an online banner is generated to include one or more productimages that are of low quality (e.g., the lighting is poor). The imagesare associated with a particular seller identifier of a seller. Becausethe machine learning system receives an indication of a lack ofselection of the online banner, the machine learning algorithm will notselect images associated with the seller identifier after a round ofpresentation of online banners and not reaching the desired result(e.g., clicks from users).

As shown in FIG. 8, method 500 includes operations 802 and 804,according to some embodiments. Operation 802 may be performed as part(e.g., a precursor task, a subroutine, or a portion) of operation 504 ofFIG. 5, in which the data representation generating module 404generates, using a machine learning algorithm, a data representation ofthe machine learning algorithm.

At operation 802, the data representation generating module 404identifies a template data feature included in the data representationof the machine learning algorithm. The template data feature representsone or more structural elements of the online banner image. In someinstances, the template data feature is associated with identifiers ofthe one or more structural elements of the online banner image in arecord of a database. In some instances, the one or more structuralelements of the online banner image are included as data features in thedata representation of the machine learning algorithm.

In various example embodiments, the one or more structural elementsincluded in the online banner image comprise at least one backgroundcolor associated with at least one portion of the online banner image,at least one alphanumeric string that is included in the at least oneportion of the online banner image, and at least one product imageassociated with a product included in an inventory of products availablefor purchase.

At operation 804, the data representation generating module 404generates the online banner image to include the one or more structuralelements represented by the template data feature.

Certain embodiments are described herein as including logic or a numberof components, modules, or mechanisms. Modules may constitute eithersoftware modules (e.g., code embodied on a machine-readable medium) orhardware modules. A “hardware module” is a tangible unit capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various example embodiments, one or morecomputer systems (e.g., a standalone computer system, a client computersystem, or a server computer system) or one or more hardware modules ofa computer system (e.g., a processor or a group of processors) may beconfigured by software (e.g., an application or application portion) asa hardware module that operates to perform certain operations asdescribed herein.

In some embodiments, a hardware module may be implemented mechanically,electronically, or any suitable combination thereof. For example, ahardware module may include dedicated circuitry or logic that ispermanently configured to perform certain operations. For example, ahardware module may be a special-purpose processor, such as aField-Programmable (late Array (FPGA) or an Application SpecificIntegrated Circuit (ASIC). A hardware module may also includeprogrammable logic or circuitry that is temporarily configured bysoftware to perform certain operations. For example, a hardware modulemay include software executed by a general-purpose processor or otherprogrammable processor. Once configured by such software, hardwaremodules become specific machines (or specific components of a machine)uniquely tailored to perform the configured functions and are no longergeneral-purpose processors. It will be appreciated that the decision toimplement a hardware module mechanically, in dedicated and permanentlyconfigured circuitry, or in temporarily configured circuitry (e.g.,configured by software) may be driven by cost and time considerations.

Accordingly, the phrase “hardware module” should be understood toencompass a tangible entity, be that an entity that is physicallyconstructed, permanently configured (e.g., hardwired), or temporarilyconfigured (e.g., programmed) to operate in a certain manner or toperform certain operations described herein. As used herein,“hardware-implemented module” refers to a hardware module. Consideringembodiments in which hardware modules are temporarily configured (e.g.,programmed), each of the hardware modules need not be configured orinstantiated at any one instance in time. For example, where a hardwaremodule comprises a general-purpose processor configured by software tobecome a special-purpose processor, the general-purpose processor may beconfigured as respectively different special-purpose processors (e.g.,comprising different hardware modules) at different times. Softwareaccordingly configures a particular processor or processors, forexample, to constitute a particular hardware module at one instance oftime and to constitute a different hardware module at a differentinstance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, the described hardwaremodules may be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over appropriate circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory device to which it iscommunicatively coupled. A further hardware module may then, at a latertime, access the memory device to retrieve and process the storedoutput. Hardware modules may also initiate communications with input oroutput devices, and can operate on a resource (e.g., a collection ofinformation).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module implemented using one or more processors.

Similarly, the methods described herein may be at least partiallyprocessor-implemented, with a particular processor or processors beingan example of hardware. For example, at least some of the operations ofa method may be performed by one or more processors orprocessor-implemented modules. Moreover, the one or more processors mayalso operate to support performance of the relevant operations in a“cloud computing” environment or as a “software as a service” (SaaS).For example, at least some of the operations may be performed by a groupof computers (as examples of machines including processors), with theseoperations being accessible via a network (e.g., the Internet) and viaone or more appropriate interfaces (e.g., an Application ProgramInterface (API)).

The performance of certain of the operations may be distributed amongthe processors, not only residing within a single machine, but deployedacross a number of machines. In some example embodiments, the processorsor processor-implemented modules may be located in a single geographiclocation (e.g., within a home environment, an office environment, or aserver farm). In other example embodiments, the processors orprocessor-implemented modules may be distributed across a number ofgeographic locations.

The modules, methods, applications and so forth described in conjunctionwith FIGS. 9 and 10 are implemented in some embodiments in the contextof a machine and associated software architecture. The sections belowdescribe representative software architecture(s) and machine (e.g.,hardware) architecture that are suitable for use with the disclosedembodiments.

Software architectures are used in conjunction with hardwarearchitectures to create devices and machines tailored to particularpurposes. For example, a particular hardware architecture coupled with aparticular software architecture will create a mobile device, such as amobile phone, tablet device, or so forth. A slightly different hardwareand software architecture may yield a smart device for use in the“internet of things.” While yet another combination produces a servercomputer for use within a cloud computing architecture. Not allcombinations of such software and hardware architectures are presentedhere as those of skill in the art can readily understand how toimplement the invention in different contexts from the disclosurecontained herein.

FIG. 9 is a block diagram 900 illustrating a representative softwarearchitecture 902, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 9 is merely a non-limiting exampleof a software architecture and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 902 may be executing onhardware such as machine 1000 of FIG. 10 that includes, among otherthings, processors 1010, memory 1030, and I/O components 1050. Arepresentative hardware layer 904 is illustrated and can represent, forexample, the machine 1000 of FIG. 10. The representative hardware layer904 comprises one or more processing units 906 having associatedexecutable instructions 908. Executable instructions 908 represent theexecutable instructions of the software architecture 902, includingimplementation of the methods, modules and so forth of FIGS. 1-8.Hardware layer 904 also includes memory and/or storage modules 910,which also have executable instructions 908. Hardware layer 904 may alsocomprise other hardware as indicated by 912 which represents any otherhardware of the hardware layer 904, such as the other hardwareillustrated as part of machine 1000.

In the example architecture of FIG. 9, the software 902 may beconceptualized as a stack of layers where each layer provides particularfunctionality. For example, the software 902 may include layers such asan operating system 914, libraries 916, frameworks/middleware 918,applications 920 and presentation layer 922. Operationally, theapplications 920 and/or other components within the layers may invokeapplication programming interface (API) calls 924 through the softwarestack and receive a response, returned values, and so forth illustratedas messages 926 in response to the API calls 924. The layers illustratedare representative in nature, and not all software architectures haveall layers. For example, some mobile or special purpose operatingsystems may not provide a frameworks/middleware layer 918, while othersmay provide such a layer. Other software architectures may includeadditional or different layers.

The operating system 914 may manage hardware resources and providecommon services. The operating system 914 may include, for example, akernel 928, services 930, and drivers 932. The kernel 928 may act as anabstraction layer between the hardware and the other software layers.For example, the kernel 928 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 930 may provideother common services for the other software layers. The drivers 932 maybe responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 932 may include display drivers,camera drivers, Bluetooth® drivers, flash memory drivers, serialcommunication drivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi®drivers, audio drivers, power management drivers, and so forth dependingon the hardware configuration.

The libraries 916 may provide a common infrastructure that may beutilized by the applications 920 and/or other components and/or layers.The libraries 916 typically provide functionality that allows othersoftware modules to perform tasks in an easier fashion than to interfacedirectly with the underlying operating system 914 functionality (e.g.,kernel 928, services 930 and/or drivers 932). The libraries 916 mayinclude system 934 libraries (e.g., C standard library) that may providefunctions such as memory allocation functions, string manipulationfunctions, mathematic functions, and the like. In addition, thelibraries 916 may include API libraries 936 such as media libraries(e.g., libraries to support presentation and manipulation of variousmedia format such as MPREG4, H.264, MP3, AAC, AMR, JPG, PNG), graphicslibraries (e.g., an OpenGL framework that may be used to render 2D and3D in a graphic content on a display), database libraries (e.g., SQLitethat may provide various relational database functions), web libraries(e.g., WebKit that may provide web browsing functionality), and thelike. The libraries 916 may also include a wide variety of otherlibraries 938 to provide many other APIs to the applications 920 andother software components/modules.

The frameworks 918 (also sometimes referred to as middleware) mayprovide a higher-level common infrastructure that may be utilized by theapplications 920 and/or other software components/modules. For example,the frameworks 918 may provide various graphic user interface (GUI)functions, high-level resource management, high-level location services,and so forth. The frameworks 918 may provide a broad spectrum of otherAPIs that may be utilized by the applications 920 and/or other softwarecomponents/modules, some of which may be specific to a particularoperating system or platform.

The applications 920 include built-in applications 940, third party,applications 942, and machine learning modules 944 (e.g., access module402, data representation generating module 404, banner image generatingmodule 406, or user interface module 408). Examples of representativebuilt-in applications 940 may include, but are not limited to, acontacts application, a browser application, a book reader application,a location application, a media application, a messaging application,and/or a game application. Third party applications 942 may include anyof the built in applications as well as a broad assortment of otherapplications. In a specific example, the third party application 942(e.g., an application developed using the Android™ or iOS™ softwaredevelopment kit (SDK) by an entity other than the vendor of theparticular platform) may be mobile software running on a mobileoperating system such as iOS™—Android™, Windows® Phone, or other mobileoperating systems. In this example, the third party application 942 mayinvoke the API calls 924 provided by the mobile operating system such asoperating system 914 to facilitate functionality described herein.

The applications 920 may utilize built in operating system functions(e.g., kernel 928, services 930 and/or drivers 932), libraries (e.g.,system 934, APIs 936, and other libraries 938), frameworks/middleware918 to create user interfaces to interact with users of the system.Alternatively, or additionally, in some systems interactions with a usermay occur through a presentation layer, such as presentation layer 944.In these systems, the application/module “logic” can be separated fromthe aspects of the application/module that interact with a user.

Some software architectures utilize virtual machines. In the example ofFIG. 9, this is illustrated by virtual machine 948. A virtual machinecreates a software environment where applications/modules can execute asif they were executing on a hardware machine (such as the machine ofFIG. 10, for example). A virtual machine is hosted by a host operatingsystem (operating system 914 in FIG. 10) and typically, although notalways, has a virtual machine monitor 946, which manages the operationof the virtual machine as well as the interface with the host operatingsystem (i.e., operating system 914). A software architecture executeswithin the virtual machine such as an operating system 9:50, libraries952, frameworks/middleware 954, applications 956 and/or presentationlayer 958. These layers of software architecture executing within thevirtual machine 948 can be the same as corresponding layers previouslydescribed or may be different.

FIG. 10 is a block diagram illustrating components of a machine 1000,according to some example embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 10 shows a diagrammatic representation of the machine1000 in the example form of a computer system, within which instructions1016 (e.g., software, a program, an application, an apples, an app, orother executable code) for causing the machine 1000 to perform any oneor more of the methodologies discussed herein may be executed. Forexample the instructions may cause the machine to execute the flowdiagrams of FIGS. 5-8. Additionally, or alternatively, the instructionsmay implement the access module 402, the data representation generatingmodule 404, the banner image generating module 406, and the userinterface module 408 of FIG. 4, The instructions transform the general,non-programmed machine into a particular machine programmed to carry outthe described and illustrated functions in the manner described. Inalternative embodiments, the machine 1000 operates as a standalonedevice or may be coupled (e.g., networked) to other machines. In anetworked deployment, the machine 1000 may operate in the capacity of aserver machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1000 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1016, sequentially or otherwise, that specify actions to betaken by machine 1000. Further, while only a single machine 1000 isillustrated, the term “machine” shall also be taken to include acollection of machines 1000 that individually or jointly execute theinstructions 1016 to perform any one or more of the methodologiesdiscussed herein.

The machine 1000 may include processors 1010, memory 1030, and I/Ocomponents 1050, which may be configured to communicate with each othersuch as via a bus 1002. In an example embodiment, the processors 1010(e.g., a Central Processing Unit (CPU), a Reduced Instruction SetComputing (RISC) processor, a Complex Instruction Set Computing (CISC)processor, a Graphics Processing Unit (GPU), a Digital Signal Processor(DSP), an Application Specific Integrated Circuit (ASIC), aRadio-Frequency Integrated Circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, processor 1012and processor 1014 that may execute instructions 1016. The term“processor” is intended to include multi-core processor that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.10 shows multiple processors, the machine 1000 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core process), multiple processors with a single core,multiple processors with multiples cores, or any combination thereof.

The memory/storage 1030 may include a memory 1032, such as a mainmemory, or other memory storage, and a storage unit 1036, bothaccessible to the processors 1010 such as via the bus 1002. The storageunit 1036 and memory 1032 store the instructions 1016 embodying any oneor more of the methodologies or functions described herein. Theinstructions 1016 may also reside, completely or partially, within thememory 1032, within the storage unit 1036, within at least one of theprocessors 1010 (e.g., within the processor's cache memory), or anysuitable combination thereof, during execution thereof by the machine1000. Accordingly, the memory 1032, the storage unit 1036, and thememory of processors 1010 are examples of machine-readable media.

As used herein, “machine-readable medium” means a device able to storeinstructions and data temporarily or permanently and may include, but isnot be limited to, random-access memory (RAM), read-only memory (ROM),buffer memory, flash memory, optical media, magnetic media, cachememory, other types of storage (e.g., Erasable Programmable Read-OnlyMemory (EEPROM)) and/or any suitable combination thereof. The term“machine-readable medium” should be taken to include a single medium ormultiple media (e.g., a centralized or distributed database, orassociated caches and servers) able to store instructions 1016. The term“machine-readable medium” shall also be taken to include any medium, orcombination of multiple media, that is capable of storing instructions(e.g., instructions 1016) for execution by a machine (e.g., machine1000), such that the instructions, when executed by one or moreprocessors of the machine 1000 (e.g., processors 1010), cause themachine 1000 to perform any one or more of the methodologies describedherein. Accordingly, a “machine-readable medium” refers to a singlestorage apparatus or device, as well as “cloud-based” storage systems orstorage networks that include multiple storage apparatus or devices. Theterm “machine-readable medium” excludes signals per se.

The I/O components 1050 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1050 that are included in a particular machine will depend onthe type of machine. For example, portable machines such as mobilephones will likely include a touch input device or other such inputmechanisms, while a headless server machine will likely not include sucha touch input device. It will be appreciated that the I/O components1050 may include many other components that are not shown in FIG. 10.The I/O components 1050 are grouped according to functionality merelyfor simplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 1050 mayinclude output components 1052 and input components 1054. The outputcomponents 1052 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1054 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

In further example embodiments, the I/O components 1050 may includebiometric components 1056, motion components 1058, environmentalcomponents 1060, or position components 1062 among a wide array of othercomponents. For example, the biometric components 1056 may includecomponents to detect expressions (e.g., hand expressions, facialexpressions, vocal expressions, body gestures, or eye tracking), measurebiosignals (e.g., blood pressure, heart rate, body temperature,perspiration, or brain waves), identify a person (e.g., voiceidentification, retinal identification, facial identification,fingerprint identification, or electroencephalogram basedidentification), and the like. The motion components 1058 may includeacceleration sensor components (e.g., accelerometer), gravitation sensorcomponents, rotation sensor components (e.g., gyroscope), and so forth.The environmental components 1060 may include, for example, illuminationsensor components (e.g., photometer), temperature sensor components(e.g., one or more thermometer that detect ambient temperature),humidity sensor components, pressure sensor components (e.g.,barometer), acoustic sensor components (e.g., one or more microphonesthat detect background noise), proximity sensor components (e.g.,infrared sensors that detect nearby objects), gas sensors (e.g., gasdetection sensors to detection concentrations of hazardous gases forsafety or to measure pollutants in the atmosphere), or other componentsthat may provide indications, measurements, or signals corresponding toa surrounding physical environment. The position components 1062 mayinclude location sensor components (e.g., a Global Position System (GPS)receiver component), altitude sensor components (e.g., altimeters orbarometers that detect air pressure from which altitude may be derived),orientation sensor components (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1050 may include communication components 1064operable to couple the machine 1000 to a network 1080 or devices 1070via coupling 1082 and coupling 1072 respectively. For example, thecommunication components 1064 may include a network interface componentor other suitable device to interface with the network 1080. In furtherexamples, communication components 1064 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1070 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 1064 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1064 may include Radio Frequency Identification(RFII)) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1064, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NFCbeacon signal that may indicate a particular location, and so forth.

In various example embodiments, one or more portions of the network 1080may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local area network (LAN), a wireless LAN (WLAN), a widearea network (WAN), a wireless WAN (WWAN), a metropolitan area network(MAN), the Internet, a portion of the Internet, a portion of the PublicSwitched Telephone Network (PSTN), a plain old telephone service (POTS)network, a cellular telephone network, a wireless network, a Wi-Fi®network, another type of network, or a combination of two or more suchnetworks. For example, the network 1080 or a portion of the network 1080may include a wireless or cellular network and the coupling 1082 may bea Code Division Multiple Access (CDMA) connection, a Global System forMobile communications (GSM) connection, or other type of cellular orwireless coupling. In this example, the coupling 1082 may implement anyof a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data. Optimized(EVDO) technology, General Packet Radio Service (CPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard setting organizations, other long rangeprotocols, or other data transfer technology.

The instructions 1016 may be transmitted or received over the network1080 using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components1064) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions1016 may be transmitted or received using a transmission medium via thecoupling 1072 (e.g., a peer-to-peer coupling) to devices 1070. The term“transmission medium” shall be taken to include any intangible mediumthat is capable of storing, encoding, or carrying instructions 1016 forexecution by the machine 1000, and includes digital or analogcommunications signals or other intangible medium to facilitatecommunication of such software.

Executable Instructions and Machine-Storage Medium

The various memories (i.e., 1030, 1032, and/or memory of theprocessor(s) 1010) and/or storage unit 1036 may store one or more setsof instructions and data structures (e.g., software) 1016 embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions, when executed by processor(s) 1010 causevarious operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” “computer-storage medium” (referred to collectively as“machine-storage medium”) mean the same thing and may be usedinterchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g.; a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data, as well as cloud-based storagesystems or storage networks that include multiple storage apparatus ordevices. The terms shall accordingly be taken to include, but not belimited to, solid-state memories, and optical and magnetic media;including memory internal or external to processors. Specific examplesof machine-storage media, computer-storage media, and/or device-storagemedia include non-volatile memory; including by way of examplesemiconductor memory devices, e.g., erasable programmable read-onlymemory (EPROM), electrically erasable programmable read-only memory(EEPROM), FPGA, and flash memory devices; magnetic disks such asinternal hard disks and removable disks; magneto-optical disks; andCD-ROM and DVD-ROM disks. The terms machine-storage media,computer-storage media, and device-storage media specifically excludecarrier waves, modulated data signals, and other such media, at leastsome of which are covered under the term “signal medium” discussedbelow. In this context, the machine-storage medium is non-transitory.

Signal Medium

The term “signal medium” or “transmission medium” shall be taken toinclude any form of modulated data signal, carrier wave, and so forth.The term “modulated data signal” means a signal that has one or more ofits characteristics set or changed in such a matter as to encodeinformation in the signal.

Computer Readable Medium

The terms “machine-readable medium,” “computer-readable medium” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and signal media. Thus, the terms includeboth storage devices/media and carrier waves/modulated data signals.

Language

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific example embodiments, various modificationsand changes may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure. Such embodimentsof the inventive subject matter may be referred to herein, individuallyor collectively, by the term “invention” merely for convenience andwithout intending to voluntarily limit the scope of this application toany single disclosure or inventive concept if more than one is, in fact,disclosed.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The Detailed Description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. Moreover, plural instances may be provided forresources, operations, or structures described herein as a singleinstance. Additionally, boundaries between various resources,operations, modules, engines, and data stores are somewhat arbitrary,and particular operations are illustrated in a context of specificillustrative configurations. Other allocations of functionality areenvisioned and may fall within a scope of various embodiments of thepresent disclosure. In general, structures and functionality presentedas separate resources in the example configurations may be implementedas a combined structure or resource. Similarly, structures andfunctionality presented as a single resource may be implemented asseparate resources. These and other variations, modifications,additions, and improvements fall within a scope of embodiments of thepresent disclosure as represented by the appended claims. Thespecification and drawings are, accordingly, to be regarded in anillustrative rather than a restrictive sense.

What is claimed is:
 1. A method comprising: receiving, by one or moredata processors, a user selection indicating one or more data featurespertaining to user behavior of a user in relation to an image of aproduct, the image of the product displayed on a website accessed by theuser; generating, by the one or more data processors using a machinelearning algorithm, a vector comprising the one or more data featurespertaining to the user behavior and a template data feature representingone or more structural elements of an online banner image, the machinelearning algorithm being trained with training data comprising knowndata combinations of user behavior data features and correspondingstructural elements of online banner images; generating, by the one ormore data processors for display at a client device in communicationwith the one or more data processors, the online banner image for theuser based on the one or more data features and the template datafeature associated with the vector, wherein the online banner image isgenerated using the one or more structural elements of the template datafeature; receiving a selection of the online banner image; updating thevector based on adding an additional feature associated with theselection of the online banner image; and updating the online bannerimage based on the updated vector.
 2. The method of claim 1, wherein theone or more structural elements comprise at least one background colorassociated with the online banner image or at least one alphanumericstring included in the online banner image.
 3. The method of claim 1,further comprising: causing display of the online banner image in a userinterface of the client device.
 4. The method of claim 1, wherein theonline banner image is associated with the product, the method furthercomprising: determining the one or more structural elements of theonline banner image based on the one or more data features pertaining tothe user behavior in relation to the image of the product.
 5. The methodof claim 1, further comprising: identifying a number of user selectionsof the online banner image and a number of user selections of theupdated online banner image; and assigning a respective weighted valueto the online banner image and the updated online banner image, therespective weighted value based on a corresponding one of the number ofuser selections.
 6. The method of claim 5, wherein the respectiveweighted value of the online banner image and the updated online bannerimage comprise at least a portion of the training data used by themachine learning algorithm.
 7. A system comprising: one or more hardwareprocessors; and a machine-readable medium for storing instructions that,when executed by the one or more hardware processors, cause the one ormore hardware processors to perform operations comprising: receiving auser selection indicating one or more data features pertaining to userbehavior of a user in relation to an image of a product, the image ofthe product displayed on a website accessed by the user; generating, bythe one or more hardware processors using a machine learning algorithm,a vector comprising the one or more data features pertaining to the userbehavior and a template data feature representing one or more structuralelements of an online banner image, the machine learning algorithm beingtrained with training data comprising known data combinations of userbehavior data features and corresponding structural elements of onlinebanner images; generating, for display at a client device incommunication with the one or more hardware processors, the onlinebanner image for the user based on the one or more data features and thetemplate data feature associated with the vector, wherein the onlinebanner image is generated using the one or more structural elements ofthe template data feature; receiving an indication of an input; updatingthe vector based on adding an additional feature that indicates theinput; and updating the online banner image based on the updated vector.8. The system of claim 7, wherein the one or more structural elementscomprise at least one background color associated with the online bannerimage or at least one alphanumeric string included in the online bannerimage.
 9. The system of claim 7, wherein the operations furthercomprise: causing display of the online banner image in a user interfaceof the client device.
 10. The system of claim 7, wherein the inputcomprises a selection of the online banner image.
 11. The system ofclaim 7, wherein the operations further comprise: identifying a numberof user selections of the online banner image and a number of userselections of the updated online banner image; and assigning arespective weighted value to the online banner image and the updatedonline banner image, the respective weighted value based on acorresponding one of the number of user selections.
 12. The system ofclaim 11, wherein the respective weighted value of the online bannerimage and the updated online banner image comprise at least a portion ofthe training data used by the machine learning algorithm.
 13. Anon-transitory machine-readable storage medium storing instructionsthat, when executed by one or more hardware processors, cause the one ormore hardware processors to perform operations comprising: receiving auser selection indicating one or more data features pertaining to userbehavior of a user in relation to an image of a product, the image ofthe product displayed on a website accessed by the user; generating, bythe one or more hardware processors using a machine learning algorithm,a vector comprising the one or more data features pertaining to the userbehavior and a template data feature representing one or more structuralelements of an online banner image, the machine learning algorithm beingtrained with training data comprising known data combinations of userbehavior data features and corresponding structural elements of onlinebanner images; generating, for display at a client device incommunication with the one or more hardware processors, the onlinebanner image for the user based on the one or more data features and thetemplate data feature associated with the vector, wherein the onlinebanner image is generated using the one or more structural elements ofthe template data feature; receiving an indication of an input; updatingthe vector based on adding an additional feature that indicates theinput; and updating the online banner image based on the updated vector.14. The non-transitory machine-readable storage medium of claim 13,wherein the one or more structural elements comprise at least onebackground color associated with the online banner image or at least onealphanumeric string included in the online banner image.
 15. Thenon-transitory machine-readable storage medium of claim 13, wherein theoperations further comprise: causing display of the online banner imagein a user interface of the client device.
 16. The non-transitorymachine-readable storage medium of claim 13, wherein the input comprisesa selection of the online banner image.
 17. The non-transitorymachine-readable storage medium of claim 13, wherein the operationsfurther comprise: identifying a number of user selections of the onlinebanner image and a number of user selections of the updated onlinebanner image; and assigning a respective weighted value to the onlinebanner image and the updated online banner image, the respectiveweighted value based on a corresponding one of the number of userselections; wherein the respective weighted value of the online bannerimage and the updated online banner image comprise at least a portion ofthe training data used by the machine learning algorithm.